A Hybrid Ant Colony and Artificial Bee Colony Optimization Algorithm-based Cluster Head Selection for IoT

Abstract

The increased utilization of Internet of Things (IoT) device in the diversified applications like industries, environmental monitoring and smart homes over the recent years necessitates the selection of cluster head among the IoT devices connected to Wireless Sensor Networks (WSNs). In this paper, a Hybrid Ant Colony and Artificial Bee Colony Optimization Algorithm-based Cluster Head Selection (HACO-ABC-CHS) technique is proposed for effective cluster head selection by eliminating the limitations of ACO and ABC in a mutual manner. The problem of the stagnation in the intensification process of ACO is prevented by utilizing employee bee agents for exploration and similarly, delayed convergence issue in onlooker bee phase of ABC is resolved by partitioning the process of exploitation into two levels through the incorporation of employee bee phase for primary level of exploitation. The experimental investigation of the proposed HACO-ABC-CHS technique has proven to be significant over the benchmarked cluster head selection approaches in terms of percentage of alive nodes, dead nodes, residual energy and throughput.